Call for Papers: Resilience of complex coupled Socio-Technical-Environmental systems through the modeling lens

Special Issue: Resilience of complex coupled Socio-Technical-Environmental systems through the modeling lens

Guest Editors: Tatiana Filatova (4TU.RE) & Tina Comes (4TU.RE), Christoph Hoelscher (FRS), and Juliet Mian (RS)

 

The global drivers of social and environmental change including urbanization, population growth, globalization, aging assets, ‘net zero’ and the changing climate are dramatically affecting our critical infrastructure systems. At the same time, they become increasingly inter-connected, digitalized and reliant on emerging technologies. These phenomena occur in the conditions of fragmented decentralized decision-making involving multiple stakeholders, and shifting policy environment and value chains, making top-down decisions and linear planning inadequate.

Therefore, we increasingly question the resilience of coupled Socio-Technical-Environmental (STE) systems. Across a range of applications – water, energy, agriculture, transport, urban sector, health or data & ICT – scholars and practitioners seek to explore how complex STE systems respond to changes by absorbing, learning, adapting and self-organizing. Uncertainty shaped by both chronic stresses and acute shocks challenges the planning, design, implementation and use of infrastructure. Traditionally our physical infrastructure has been designed to be robust and to last for decades, meaning that decisions can be locked-in for the long term.  The increasing pressure both on and from the environment, the interests of diverse stakeholders, new and evolving governance structures and social institutions, require a closer look at cross-scale interactions and feedbacks between social, technical and environmental components in these complex systems. Robustness alone is no longer sufficient.

The goal of this Special Issue is to bring together cutting-edge research and international practice to offer insights into the latest scientific modeling methods, gaps, challenges and opportunities and best practice examples relating to operationalizing resilience across a range of STE applications. This Special Issue focuses on the modeling aspects across a range of methods (simulation, optimization, data analytics & machine learning, and analytical, statistical, conceptual or participatory modeling) or the use of models for supporting a dialog among practitioners and policy-makers. Case-study oriented, methodological and review articles contributing to the following themes are of particular interest:

  • Urban resilience;
  • Resilience of agricultural systems;
  • Water: clean water and sanitations, flood risk and resilience;
  • Energy, particularly the energy transition;
  • Resilience of smart and interlinked transportation and mobility systems;
  • Business, organizational, logistics and supply chain networks resilience;
  • Climate-resilient development of STE systems in light of transformations;
  • Resilience and decision-making & planning under uncertainty;
  • Data analytics and machine learning to understand social resilience;
  • Distributional and ethical aspects of resilience.

We especially welcome contributions that (1) form inter-and transdisciplinary alliances, (2) combine applications across scales, sectors and disciplines (e.g. water-food-energy nexus, mitigation-adaptation nexus), or (3) actively apply resilience concepts in practice. The suggested topics provide a guideline of the scope but should not serve as a limitation. Hence, if you have an idea for a paper that raises an important issue related to the resilience of coupled Socio-Technical-Environmental systems, please contact the Guest Editors or send your expression of interest by submitting the Extended Abstract directly.

 

Timeline

The deadline for submission of Full Papers (10-12 pages long) is Oct 31st 2021. If you are interested in participating and have any questions regarding the Special Issue, please send an email to Tatiana Filatova or any of the other Guest Editors.